The present invention relates to image processing. It finds particular application in conjunction with classification of images between natural pictures and synthetic graphics, and will be described with particular reference thereto. However, it is to be appreciated that the present invention is also amenable to other like applications.
During the past several decades, products and services such as TVs, video monitors, photography, motion pictures, copying devices, magazines, brochures, newspapers, etc. have steadily evolved from monochrome to color. With the increasing use of color products and services, there is a growing demand for “brighter” and more “colorful” colors in several applications. Due to this growing demand, display and printing of color imagery that is visually pleasing has become a very important topic. In a typical color copier application, the goal is to render the scanned document in such a way that it is most pleasing to the user.
Natural pictures differ from synthetic graphics in many aspects, both in terms of visual perception and image statistics. Synthetic graphics are featured with smooth regions separated by sharp edges. On the contrary, natural pictures are often noisier and the region boundaries are less prominent. In processing scanned images, it is sometime beneficial to distinguish images from different origins (e.g., synthetic graphics or natural pictures), however, the origin or “type” information about a scanned image is usually unavailable. The “type” information should be automatically extracted from the scanned image. This “type” information is then used in further processing of the images. High-level image classification can be achieved by analysis of low-level image attributes geared for the particular classes. Coloring schemes (e.g., gamut-mapping or filtering algorithms) are tailored for specific types of images to obtain quality reproduction. Once an image has been identified as a graphics image, further identification of image characteristics can be used to fine-tune the coloring schemes for more appealing reproductions. The most prominent characteristics of a graphics image include patches or areas of the image with uniform color and areas with uniformly changing colors. These areas of uniformly changing color are called sweeps.
Picture/graphics classifiers have been developed to differentiate between a picture image and a graphics image by analyzing low-level image statistics. For example, U.S. Pat. No. 5,767,978 to Revankar et al. discloses an adaptable image segmentation system for differentially rendering black and white and/or color images using a plurality of imaging techniques. An image is segmented according to classes of regions that may be rendered according to the same imaging techniques. Image regions may be rendered according to a three-class system (such as traditional text, graphic, and picture systems), or according to more than three (3) image classes. In addition, only two (2) image classes may be required to render high quality draft or final output images. The image characteristics that may be rendered differently from class to class may include half toning, colorization and other image attributes.
Graphics are typically generated using a limited number of colors, usually containing only a few areas of uniform colors. On the other hand, natural pictures are more noisy, containing smoothly varying colors. A picture/graphics classifier can analyze the colors to distinguish between picture and graphics images.
Graphics images contain several areas of uniform color, lines drawings, text, and have very sharp, prominent, long edges. On the other hand, natural pictures are very noisy and contain short broken edges. A picture/graphics classifier can analyze statistics based on edges to distinguish between picture and graphics images.
Classifiers that can be used to solve a certain classification problem include statistical, structural, neural networks, fuzzy logic, and machine learning classifiers. Several of these classifiers are available in public domain and commercial packages. However, no single classifier seems to be highly successful in dealing with complex real world problems. Each classifier has its own weaknesses and strengths.
The picture/graphics classification methods described above each use features of the image to make a “binary” classification decision (i.e., picture or graphics). The binary classification result is then used to “switch” between image processing functions. However, using the current set of features and the binary classification scheme, the classification accuracy, as tested on large image sets, is not perfect. Even with improved features and the binary classification scheme, it may not be possible to achieve perfect classification. In fact, there are images for which a clear classification cannot even be made by a human observer. Under such circumstances, the binary decision is often wrong, and could lead to objectionable image artifacts.
U.S. Pat. No. 5,778,156 to Schweid et al. discloses an improved method of image processing utilizing a fuzzy logic classification process. The disclosure includes a system and method to electronically image process a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes. This process uses classification to determine a membership value for the pixel for each image class and generates an effect tag for the pixel based on the fuzzy classification determination. The pixel is image processed based on the membership vector of the pixel. The image processing may include screening and filtering. The screening process screens the pixel by generating a screen value according to a position of the pixel in the set of digital image data; generating a screen amplitude weighting value based on the values in the membership vector for the pixel; multiplying the screen value and the screen amplitude weighting value to produce a modified screen value; and adding the modified screen value to the pixel of image data. The filtering process filters the pixel by low-pass filtering the pixel; high-pass filtering the pixel; non-filtering the pixel; multiplying each filtered pixel by a gain factor based on the values in the membership vector associated with the pixel; and adding the products to produce a filtered pixel of image data.
The present invention contemplates new and improved methods for classifying images that overcome the above-referenced problems and others.